"We're in the early days of the process of figuring which [database] engines match best to which workloads," Andy Palmer, co-founder of database company Vertica Systems and former CIO of Infinity Pharmaceuticals, told CNET at a recent Big Data event organized by the Massachusetts Technology Leadership Council.

The thought - at least from Big Data entrepreneurs - is that Big Data will prove disruptive to legacy vendors, giving startups an opening into this emerging market.

You may wonder whether Big Data will eventually be subsumed into existing Big Vendor offerings. That happened a fat decade ago, GigaOm recently pointed out, when new database companies popped up around the idea that relational databases couldn't handle objects well. Eventually, the article notes, big database vendors "pushed and shoved at least some object capabilities into their databases, and those smaller companies disappeared."

But many business veterans in the database market say that won't be possible this time, including MIT Professor Michael Stonebaker and Andy Palmer, both long-time entrepreneurs of new database systems.

Certainly, the big vendors like Oracle and IBM don't seem to be headed in that direction - they, too, are joining the market with products specially designed for Big Data.

"What's different about big data is that it's driven by the Web and the Internet," Kelly Stirman, vice president of customer solutions at Hadapt, told CNET. "All the Web companies tried to use Oracle [databases] to solve their problems but eventually gave up."

Kernochan argues that Big Data is being oversold and as a result, he contends few people understand its limitations. He predicts Big Data will become a supplement, not a critical part, of enterprise architectures.

" Handling Big Data is likely to require a careful mix of relational and non-relational, data-center and extra-enterprise business intelligence, with relational in-enterprise BI taking the lead role. And as the limits to parallel scalability of Hadoop and the like become more and more evident, the use of SQL-like interfaces and relational databases within Big Data use cases will become more frequent, not less," Kernochan said. "Therefore, I believe that Hadoop and its brand of Big Data will always remain a useful but not business-critical adjunct to an overall business intelligence and information management strategy."

My sense is that Big Data experts would disagree that relational databases would be usurped by Big Data. I do think they'd disagree with him about its business-critical role.

To be honest, Kernochan's piece delves into some very technical areas, and he's drawing on computational logic he worked out in college, which makes it difficult for the average reader, myself included, to follow. Techies should enjoy it, though, and will find his discussion on NoSQL's limitations and ties to relational databases a unique take on the topic.

That said, I will say he reaches some conclusions I can disagree with - specifically, his statement that Big Data is "effectively out there in the cloud' and therefore outside the usual walls of enterprise data centers." There are internal use cases for Big Data, since many organizations do have their own stores of Big Data, whether it's from sensor information or government agencies combining data.

For most of you, the bottom line is this: Big Data solutions are still emerging and evolving. At this point, it's hard to tell what organisms will survive, thrive or perish as they hit the hard shores of the enterprise market. Fortunately, many of your options are open source and most enterprise app vendors are now offering Hadoop plug-ins that will make it easier to use. Still, know that we're in the early days - possibly more early than we can even appreciate - so focus more on proven use cases and what your IT staff has the skills to handle than on marketing hype.

Loraine, you make a good point that big data solutions are still evolving and that proven use cases are important. I'd like to point out that HPCC Systems-designed by data scientists - is a data intensive supercomputing platform to process and solve Big Data analytical problems. It is a mature platform and provides for a data delivery engine together with a data transformation and linking system. The main advantages over other alternatives are the real-time delivery of data queries and the extremely powerful ECL language programming model. More info at hpccsystems.com

Darn, just saw this. A very good piece that, as they say, caused me to think. You may well be right (not about whether we disagree, but about whether there is some Big Data inside the org and whether there are mini-Hadoops springing up in divisions).

The question I'd pose you, which I couldn't include in the article because of space limitations, is this: Some of the data virtualization folks are telling me that Big Data in the public cloud is now fragmenting, so that enterprises must consider providing a separate access to each provider's Big Data and combining it somewhere as it is brought in-house -- like, 3-10 of such "gateways". Does that really sound like it's a single "virtual data store" that gives you most of your business-critical data?